Methods for creating and distributing art-directable continuous dynamic range video

ABSTRACT

Novel systems and methods are described for creating, compressing, and distributing video or image content graded for a plurality of displays with different dynamic ranges. In implementations, the created content is “continuous dynamic range” (CDR) content—a novel representation of pixel-luminance as a function of display dynamic range. The creation of the CDR content includes grading a source content for a minimum dynamic range and a maximum dynamic range, and defining a luminance of each pixel of an image or video frame of the source content as a continuous function between the minimum and the maximum dynamic ranges. In additional implementations, a novel graphical user interface for creating and editing the CDR content is described.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/861,587, filed on Sep. 22, 2015, which claims priority to U.S.Provisional Application No. 62/169,465 filed on Jun. 1, 2015 which areincorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates generally to high dynamic range (HDR)video techniques, and more particularly, some embodiments relate tomethods for creating and distributing continuous dynamic range video.

DESCRIPTION OF THE RELATED ART

A high dynamic range (HDR) is used to refer to content and displays thathave a higher luminance or brightness level and/or better contrast ratiothan standard dynamic range (SDR) content and displays.

BRIEF SUMMARY OF THE DISCLOSURE

According to various embodiments, systems and methods are disclosed forcreating and distributing video or image content graded for a pluralityof displays with different dynamic ranges. In one embodiment, creationof CDR content includes: receiving a source image and creating acontinuous dynamic range image by defining a luminance of each pixel ofthe source image as a continuous function based on a minimum dynamicrange and a maximum dynamic range. In implementations of thisembodiment, creating the continuous dynamic range image further includesgrading the source image for the minimum dynamic range and the maximumdynamic range. The source image may be a standalone image (e.g., aphotograph) or a video frame corresponding to a video.

In embodiments, the continuous dynamic range image may be compressed byapproximating each of the continuous functions using a truncatedpolynomial series. In particular implementations of this embodiment, thepolynomial series is a Chebyshev polynomial series. In furtherembodiments, the polynomial coefficients of the truncated polynomialseries may be represented in an image-like format.

In another embodiment of the technology disclosed herein, a graphicaluser interface method for creating continuous dynamic range images orvideo, includes: displaying on one or more displays of a computersystem: a plurality of graded versions of an image, where each of thegraded versions is graded for a different dynamic range display; and acontrol for modifying a continuous function defining the luminance of afirst set of pixels of the image as a continuous function based on aminimum dynamic range and a maximum dynamic range. The method furtherincludes receiving user input at the computer system actuating thecontrol for modifying the continuous function; and in response toreceiving the user input actuating the control for modifying thecontinuous function, the computer system displaying a modified versionof each of the plurality of graded versions of the image on the one ormore displays.

In yet another embodiment of the technology disclosed herein, a methodof distributing a continuous dynamic range video comprising video framesincludes the step of distributing to each of a plurality of receiverswith an associated display: a minimum dynamic range grading of each ofthe plurality of video frames; a maximum dynamic range grading of eachof the plurality of video frames; and metadata defining a luminance ofeach pixel of each of the plurality of video frames as an approximationof a continuous function between the minimum and the maximum dynamicrange. In implementations of this embodiment, the continuous dynamicrange video is transmitted as an over-the-air broadcast televisionsignal, as a satellite television network signal, or as a cabletelevision network signal. Alternatively, the continuous dynamic rangevideo may be transmitted by a content server of a computer network.

In yet a further embodiment of the technology disclosed herein, a methodof decoding a continuous dynamic range image for display on a displayhaving an associated dynamic range includes the steps of: receiving anencoded continuous dynamic range image; decoding the continuous dynamicimage using a codec; and creating a particular dynamic rangerepresentation of the image based on the decoded continuous dynamicrange image and the dynamic range of the display. In this embodiment,the received encoded continuous dynamic range image includes: a minimumdynamic range graded version of an image; a maximum dynamic range gradedversion of the image; and continuous dynamic range metadatacorresponding to the image.

Other features and aspects of the disclosed method will become apparentfrom the following detailed description, taken in conjunction with theaccompanying drawings, which illustrate, by way of example, the featuresin accordance with embodiments of the disclosure. The summary is notintended to limit the scope of the claimed disclosure, which is definedsolely by the claims attached hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure, in accordance with one or more variousembodiments, is described in detail with reference to the followingfigures. The figures are provided for purposes of illustration only andmerely depict typical or example embodiments of the disclosure.

FIG. 1 illustrates an example environment in which CDR video may becreated, encoded, and distributed in accordance with the presentdisclosure.

FIG. 2 illustrates an example CDR video creation and encoding systemthat may be implemented in the environment of FIG. 1.

FIG. 3 is an operational flow diagram illustrating a method of creating,encoding, and distributing CDR video in accordance with an embodiment ofthe present disclosure.

FIG. 4 is an operational flow diagram illustrating an example method ofcreating CDR video in accordance with the present disclosure.

FIG. 5 illustrates a representation of a dynamic range hull inaccordance with the present disclosure.

FIG. 6 illustrates an example implementation of lumipaths for a videoframe in accordance with the present disclosure.

FIG. 7A illustrates an example video editing interface that may be usedby an artist to create CDR video in accordance with the presentdisclosure.

FIG. 7B illustrates an example video editing interface that may be usedby an artist to create CDR video in accordance with the presentdisclosure.

FIG. 8 illustrates a process of obtaining a numerical lumipath inaccordance with the present disclosure.

FIG. 9 is an operational flow diagram illustrating an example method ofcompressing and encoding CDR video in preparation for distribution inaccordance with the present disclosure.

FIG. 10 illustrates an example approximation of a function by Chebyshevpolynomials of different orders in accordance with the presentdisclosure.

FIG. 11 illustrates the first eight coefficient images of a frame of avideo sequence in accordance with example embodiment of the presentdisclosure.

FIG. 12 is an operational flow diagram illustrating a receiver sidemethod for decoding and displaying a received CDR video content streamin accordance with the present disclosure.

FIG. 13 illustrates an example computing module that may be used toimplement various features of the methods disclosed herein.

The figures are not exhaustive and do not limit the disclosure to theprecise form disclosed.

DETAILED DESCRIPTION

The emergence of HDR displays from multiple vendors with differentdynamic ranges creates some significant challenges for contentproduction and distribution. Specifically, the production challenge istailoring HDR content to a number of upcoming displays that areannounced to have peak luminances ranging from 800-4000 nits, as well asfuture HDR displays with different dynamic ranges. The straightforwardapproach of grading content for each specific display dynamic range doesnot scale well due to the required additional manual labor. Methodsproposed in literature, such as display adaptive tone mapping, canalleviate this issue, but do not allow for precise artistic freedom inthe expression of brightness variations.

Additionally, the distribution challenge with respect to HDR content isthe task of efficiently coding and transmitting a large number of HDRstreams graded for different display dynamic ranges. Previous workproposed distributing a single HDR stream efficiently as a residualsignal over the SDR content. This approach, however, is not efficientfor application in the emerging landscape where numerous HDR streamsneed to be transmitted simultaneously.

In accordance with embodiments of the technology disclosed herein, novelsystems and methods are disclosed for creating video or image contentgraded for a plurality of displays with different dynamic ranges. Inembodiments, the created content is “continuous dynamic range” (CDR)content—a novel representation of pixel-luminance as a function ofdisplay dynamic range. In these embodiments, the creation of CDR contentincludes grading a source content for a minimum dynamic range and amaximum dynamic range, and obtaining a continuous dynamic range of thesource content by defining a luminance of each pixel of an image orvideo frame of the source content as a continuous function between theminimum and the maximum dynamic ranges.

In this manner, content simultaneously graded for all possible displaydynamic ranges may be created with little overhead. In theseembodiments, a graphical user interface may be provided whereby a userspecifies how the pixel luminances vary for different dynamic ranges,thereby allowing the creation of CDR video or images with full artisticcontrol.

In further embodiments of the technology disclosed herein, methods aredescribed for compressing, encoding, and distributing created CDR video.In these embodiments, the CDR video may be distributed as 1) a maximumdynamic range grading of the video; 2) a minimum dynamic range gradingof the video; and 3) metadata defining a luminance of each pixel of eachvideo frame as a polynomial series approximation of a continuousfunction between the minimum and the maximum dynamic ranges.

As used herein to describe displays, the term “dynamic range” generallyrefers to the display's luminance range—the range from the display'sminimum luminance (i.e., “black level”) and peak luminance. As would beunderstood by one having skill in the art, luminance may be measuredusing any known system of units such as SI units of candela per squaremeter (cd/m²) or non-SI units of nits.

As further used herein, the term “lumipath” refers to a function thatrepresents a pixel's luminance value as a function of the peak luminanceof a target display.

Before describing the invention in detail, it is useful to describe anexample environment in which the invention can be implemented. FIG. 1illustrates one such example environment 100. In environment 100, sourcevideo 101 (e.g., video in raw camera format) is created and encoded(step 110) as continuous dynamic range video using CDR video creationand encoding system 102. The source video may comprise a film, atrailer, an episode from a series, a commercial, a video game cutscene,and the like.

In embodiments, further described below, a user of CDR video creationand encoding system 102 may utilize a graphical user interface (GUI) tospecify how the pixel luminances of each video frame of the source video101 vary for different dynamic ranges, thereby allowing the creation ofCDR video with full artistic control over the appearance of the videofor different dynamic range displays.

Following creation of the CDR video, at step 120, the CDR video isdistributed to a plurality of receivers 121-123 for decoding and display(step 130). In the CDR encoded source video, the pixel-luminance of eachframe is defined as a function of dynamic range. Accordingly, dependingon the dynamic range of the display associated with receivers 121-123, areceiver 121-123 may decode the CDR based on the dynamic range of thereceiver's display (illustrated by the rectangular pattern on the leftside of the display in FIG. 1). As illustrated in particular environment100, distribution step 120 comprises streaming or transmitting the CDRvideo to a plurality of television sets (e.g., smart television sets) ormonitor displays 121-123 over an electronic distribution network 115. Inalternative embodiments, the CDR video may be transmitted to otherreceivers capable of decoding and displaying the CDR video such as, forexample, smartphones, laptops, tablets, workstations, and the like.

In various embodiments, the CDR video may be transmitted as anover-the-air broadcast television signal, a satellite television networksignal, or a cable television network signal. Alternatively, the CDRvideo may be transmitted by a content server over a computer network. Aswould be appreciated by one having skill in the art, electronicdistribution network 115 may include any combination of communicationmediums, such as, for example, a coaxial cable system, a fiber opticcable system, an Ethernet cable system, a satellite communicationsystem, a cellular communication system, and the like. In yet furtherembodiments, the CDR video may be distributed using physical media suchas a solid state drive, magnetic tape, cartridge, a Blu-ray disc orother fixed or removable storage media known in the art that may storevideo.

FIG. 2 illustrates an example CDR video creation and encoding system 102that may be implemented in environment 100. In various embodiments,system 102 may be any computing system (workstation, laptop, smartphone,etc.) configured to receive a source video and create a CDR video thatcan be tailored for a plurality of different displays having differentdynamic ranges. As illustrated, system 102 includes a connectivityinterface 131, storage 132 for storing a CDR video creation application133 and source video 101, processor 134, and one or more displays 135.Connectivity interface 131 may connect system 102 to a contentdistribution network (e.g., network 115) using a wireless networkconnection such as a local area network connection, a cellular networkconnection, a satellite network connection, or the like. Additionally,connectivity interface may include a physical interface for transferringand receiving information such as, for example, a USB interface.

Processor 134 executes a CDR video creation application 133 that mayprovide a graphical user interface for an artist to customize videocontent for displays having different dynamic ranges. In suchembodiments, the artist may specify how the pixel luminances ofdifferent regions of each video frame of source video 101 vary fordifferent dynamic ranges. In implementations of these embodiments,further described below, display 135 displays multiple dynamic rangeversions of a video frame along with various user controls forcontrolling the pixel luminance of each video frame. In someembodiments, CDR video creation application 133 may be integrated aspart of an animation application, a video editing application, an imageediting application, a video game design application, or somecombination thereof.

In further embodiments, processor 134 may compress and encode thecreated CDR video (e.g., through CDR video creation application 133) inpreparation for distribution using system 102 or another distributionmeans.

Although example environment 100 was described with respect to thecreation, encoding, and distribution of CDR video, it should be notedthat in other embodiments the invention may be implemented in anenvironment that focuses on the creation and distribution of CDR imagessuch as photographs, computer-generated images, and the like. As wouldbe appreciated by one having skill in the art, environment 100 could beadapted for the creation, encoding, and distribution of CDR images.

FIG. 3 illustrates a method 200 of creating, encoding, and distributingCDR video in accordance with an embodiment of the technology disclosedherein. Method 200 takes as an input a source video 201 and outputs aCDR video that includes content simultaneously graded for all possibledisplay dynamic ranges. The distributed CDR video includes an encodedminimum dynamic range grading of the video 207, an encoded maximumdynamic range grading of the video 208, and encoded metadata 209 thatmay be used by a receiving display to tailor the received CDR video toits dynamic range.

Method 200 will be described in conjunction with FIGS. 4 and 9, whichillustrate particular methods of creating the CDR video (220) andcompressing and encoding the created CDR video (230). In variousembodiments, some or all of the process operations of method 200 (e.g.,CDR video creation, compression, and encoding, but not distribution) maybe implemented by system 102 using CDR video creation application 133.

Method 200 begins with receiving source video 201 at operation 210. Inembodiments, the received source video may be in a raw camera format(e.g., source video for a film, series episode, commercial, etc.) or acomputer-generated source video (e.g., source video for an animationfilm, video game, and the like). For example, the source video may bereceived from a studio after filming a scene or from a computer graphicsartist after animating a scene. In various embodiments, the dynamicrange of the source video may be arbitrary. In a particular embodiment,the source video may comprise high dynamic range (HDR) content with upto 14 f-stops.

Following reception of the source video 201, at operation 220 CDR videois created. FIG. 4 illustrates one example method 220 of creating theCDR video. At operation 211 the source video content 201 is graded forminimum and maximum target dynamic ranges, thereby creating a minimumdynamic range graded content 202 and maximum dynamic range gradedcontent 203. In embodiments, this grading process includes defining thebrightness and color of the source video for the maximum and minimumdynamic range. During, before, or after this grading process, a “dynamicrange hull” may be determined for the content of source video 201. Asthe dynamic range continuum encompassed by a CDR video is a superset ofthe dynamic ranges of all target displays, the dynamic range hulldefines the dynamic range continuum between the minimum and maximumdynamic ranges.

FIG. 5 illustrates a representation the dynamic range hull. Asillustrated, the minimum dynamic range is bounded by the minimum peakluminance (b) and maximum black level (d) among a set of all targetdisplay dynamic ranges. Analogously, the maximum dynamic range isbounded by the max peak luminance (a) minimum black level (c) of the setof all target display dynamic ranges. In various embodiments, system 102may determine the dynamic range hull based on parameters such as thetype of content of source video 102 (e.g., cinematic film, TV episode,TV commercial, etc.), a list of known dynamic ranges of target displays,and other parameters.

For example, for a plurality of target displays consider the case wherethe lowest peak luminance is 100 nits, the highest peak luminance is4000 nits, the minimum black level is 0.01 nits, and the maximum blacklevel is 0.1 nits. In this example, the minimum dynamic range would bebounded by 0.1 nits to 100 nits, and the maximum dynamic range would bebounded by 0.01 nits to 4000 nits. Analogously, the maximum gradedcontent would be targeted for a display with peak luminance of 4000 nitsand a black level of 0.01 nits, and the minimum graded content would betargeted for a display with a peak luminance of 100 nits and a blacklevel of 0.1 nits.

Subsequently, at operation 212 a CDR video may be created by defining aluminance of each pixel of each frame of the source video as acontinuous function based on the minimum and maximum dynamic rangegradings 202, 203. In these embodiments, the CDR video may store adynamic range function for each pixel as contrasted with conventionalmethods that store a scalar luminance value for each pixel. Inparticular embodiments, a “lumipath”, a function that represents apixel's luminance value as a function of the peak luminance of a targetdisplay, may be used and stored at each pixel.

FIG. 6 illustrates an example implementation of lumipaths for a videoframe 500 including pixels 501-503. As shown, each pixel 501-503 ofvideo frame 500 has an associated lumipath function 501A-503A thatdefines the luminance of the pixel based on the peak display luminanceof a target display. In this example implementation, minimum dynamicrange 510 and maximum dynamic range 530 bound the lumipath functions501A-503A. Dynamic range 520 corresponds to an intermediate dynamicrange display. For pixel 501, the pixel luminance does not significantlyincrease as the peak display luminance increases, indicating a darkerarea of video frame 500. By contrast, for pixels 502 and 503 the pixelluminance dramatically increases around the center of peak displayluminances, and levels off thereafter. As would be appreciated by onehaving skill in the art, in various embodiments the shape of lumipathfunctions 501A-503A may be varied to tailor the appearance of the videocontent for different displays.

In embodiments, the lumipaths for each pixel may be user-defined usingan interactive video editing interface provided by CDR video creationapplication 133. FIGS. 7A-7B illustrate one particular implementation ofa video editing interface that may be used by a user of system 102 todefine the lumipaths of each pixel of each video frame. As illustratedin this particular embodiment, a first SDR display (illustrated by FIG.7A) provides an interface 600 including controls (e.g., buttons,toggles, sliders, navigational components, etc.) for defining thelumipaths for different video frames for different display dynamicranges. A second HDR display (illustrated by FIG. 7B) provides aninterface 610 that allows users to visualize their edits for a pluralityof dynamic ranges in an interactive manner. It should be noted thatalthough separate displays are illustrated for defining the lumipathsand displaying the edits for a plurality of dynamic ranges, inalternative embodiments a single display (e.g., a large HDR display) maybe used to perform both functions.

Interface 600 include controls 605 for loading graded videos into thesystem (e.g., by selecting folders) for beginning a grading session. Asillustrated in this embodiment, the system is loaded with gradings forthe extremes of the dynamic range hull, i.e., the minimum graded videoand the maximum graded video. Additionally, interface 600 includesbutton control 601 for selecting a highest graded video or video frame(601), button control 602 for selecting a lowest graded video or videoframe 602, slider control 603 for selecting a particular display with anassociated dynamic range, and slider control 604 for selecting aparticular video frame.

Interface 610 provides a tiled interface for visualizing continuousdynamic range video over several dynamic ranges. In this exampleembodiment, six different windows allow the user to visualize what thevideo or video frame will look like over six different dynamic rangedisplays. By selecting a frame mode or video mode provided by control609, a user may visualize what a particular frame or video segment willlook like. In this implementation, the dynamic ranges are shown inascending order from top left to bottom right, with the top left window611 showing minimum dynamic range grading and the bottom right window612 showing the maximum dynamic grading. Alternatively, in otherimplementations any number of windows and any dynamic range displayorder may be used.

In embodiments illustrated by interface 600, cascaded masks may allow auser of application 133 to define a magnitude of edits in each videoframe. As shown, a mask interface may provide controls 606 for selectinga particular mask, a display 607 of the selected mask, and controls 608for modifying a lumipath function corresponding to the selected mask. Inimplementations, the masks may be applied to contrasting regions of avideo frame, a specific region of the video frame, or the entire videoframe.

For example, consider the scene illustrated in FIG. 7B. A first globalmask may generate an even masking for every pixel of the scene includingthe animated character's face and the background environment. Once auser is satisfied with the global mask, another mask (e.g., mask 607)may be applied to separate all or a portion of the character's face fromthe rest of the scene, allowing for precise local control. For example,the character's face may be made brighter or darker relative to theenvironment across the entire dynamic range hull. Alternatively, a usermy choose to adjust the appearance of the animated character's face fora particular dynamic range (e.g., one or two target displays) as opposedto the entire dynamic range. As would be appreciated by one having skillin the art considering the above-described examples, the masks may beused to change highlights or shadows of different video frames acrossdifferent dynamic ranges.

As shown in the particular implementation of interface 600, a thirddegree polynomial spline interface control 608 allows a user to manuallyinput and modify lumipaths by changing the shape of the displayedlumipath function (e.g., by selecting control points on the curve anddragging the mouse). However, in other implementations other suitableinterfaces (e.g., higher degree polynomial spline interfaces) known inthe art for defining and modifying continuous functions may be provided.It should also be noted that although the particular example of FIGS.7A-7B is described with respect to using lumipath functions to define apixel's luminance across the dynamic range hull, in other embodimentsother suitable continuous functions may be used for defining a pixel'sluminance based on the minimum and maximum dynamic range gradings. Itshould also be noted that in alternative implementations lumipathfunctions may be predefined and used to generate CDR video based on aminimum and maximum grading without relying on any user interaction.

In embodiments, functions that define the luminance of each pixel ofeach frame, for example lumipaths, may be mathematically defined asfollows. First, the minimum and maximum gradings 202, 203 may be denotedas I_(α) and I_(β), respectively. The minimum and peak luminances ofI_(α) are denoted as η_(α) and π_(α), respectively, and the minimum andpeak luminance of I_(β) are denoted as η_(β) and π_(β), respectively.Functions that specify how the pixel luminances change across a dynamicrange hull may be defined by Equation (1):h ^(p):[η_(α),η_(β)]×[π_(α),π_(β)]→[

(I _(α) ^(p)),

(I _(β) ^(p))]  (1)Which associates with each pixel p and dynamic range (η,π) a uniqueluminance value h^(p)(η,π), where

(I_(α) ^(p)) is the luminance of a pixel p for the minimum grading, and

(I_(β) ^(p)) is the luminance of a pixel p for the maximum grading.Accordingly, Equation (1) maps the luminance of a pixel in any targetdynamic range of the dynamic range hull to a value that is in betweenthe actual pixel luminance values in the minimum and maximum gradings.

To reduce the computational complexity of generating these functions andthe amount of distributed data, the domain may be restricted to [π_(α),π_(β)], and the associated minimum luminance for any π∈[π_(α), π_(β)]may be defined by Equation (2):

$\begin{matrix}{{\eta(\pi)} = {\eta_{\alpha} + {\left( {\eta_{\beta} - \eta_{\alpha}} \right)\;\frac{\pi - \pi_{\alpha}}{\pi_{\beta} - \pi_{\alpha}}}}} & (2)\end{matrix}$Following Equation (2), the considered dynamic range hull may be definedby (η/(π),π)∀π∈[π_(α),π_(β)]. Consequently, a lumipath, which representsa pixel's luminance value as a function of the peak luminance pi of atarget display, may be defined by Equation (3):g ^(p):[π_(α),π_(β)]→[

(I _(α) ^(p)),

(I _(β) ^(p))]  (3)Where π_(α) and π_(β) are the peak luminances corresponding to themaximum and minimum dynamic ranges.

As noted in the example of FIGS. 6A-6B, users may select desired imageregions by using masks and adjusting the lumipaths by modifying controlpoints of a third degree polynomial spline interface. More generally,the user's use of masks to define the lumipaths may be mathematicallydefined as follows. Formally, given a series of image masks M_(j) withvalues M_(j) ^(p)∈[0,1], the user may manually specify functionsk_(j):[π_(α),π_(β)]→[π_(α),π_(β)] with the user interface. When appliedto each pixel, the function is modulated at each pixel position by themask, and k_(j) ^(p) is obtained as shown in Equation (4):k _(j) ^(p)(π)=M _(j) ^(p) k _(j)(π)+(1−M _(j) ^(p))π  (4)Equation (4) defines a blending between the artist's defined curve and alinear curve based on the weights specified by the mask, allowing forsmoothly varying edits. Accordingly, by employing n masks and specifyingn such functions, the corresponding lumipaths g) may be obtained byapplying all functions successively (layer based grading) and scalingthe result as shown by Equation (5):

$\begin{matrix}{g^{p} = {{\frac{{k_{1}^{p} \circ \ldots \circ k_{n}^{p}} - \pi_{\alpha}}{\pi_{\beta} - \pi_{\alpha}}\left( {{\mathcal{L}\left( I_{\beta}^{p} \right)} - {\mathcal{L}\left( I_{\alpha}^{p} \right)}} \right)} + {\mathcal{L}\left( I_{\alpha}^{p} \right)}}} & (5)\end{matrix}$Where the lumipath g^(p):[π_(α),π_(β)]→[

(I_(α) ^(p)),

_(β) ^(p))] is the desired curve defining the luminance of the pixel pfor any display with maximum brightness between the two analyzedextremes. FIG. 8 illustrates this process of obtaining a numericallumipath as defined by equations (4) and (5). As shown, lumipaths inputby an artists are averaged with linear functions according to theweights specified in the user interface and subsequently concatenated toobtain the final per-pixel lumipath g^(p).

As would be appreciated by one having skill in the art, as there are norestrictions in how the input gradings of the video frames are obtained,any number of pixel-level masks for region selection can be used as longas spatial correspondence of the pixels is conserved. Additionally, thelumipaths or other functions may be defined precisely using any numberof control points, thereby allowing significant artistic freedom duringoperation 220.

Following creation of CDR video at operation 220, the CDR video in itsraw format is represented for each frame f by (1) a minimum dynamicrange graded image (e.g., I_(α) ^(f)); 2) a maximum dynamic range gradedimage (e.g., I_(β) ^(f)); and 3) metadata including a continuous dynamicrange function (e.g., lumipaths g^(p,f)) for every pixel of the frame.In embodiments, the raw format of this data make occupy a considerableamount of data. Accordingly, at operation 230 of method 200 the CDRvideo may be compressed and encoded in preparation for distribution.

FIG. 9 illustrates one example method 230 of compressing and encodingthe CDR video in preparation for distribution. As further describedbelow, method 230 may be used in embodiments to provide a representationof the CDR functions for each pixel that is both data efficient andvisually lossless as compared to the original CDR video.

Method 230 begins at operation 231, where the CDR functionscorresponding to each pixel are approximated using a polynomial seriesthat is truncated after a certain number of coefficients. Accordingly,the resulting representation of each CDR function is a finite set ofcoefficients (i.e., vector of coefficients) with respect to a polynomialbasis. In preferred embodiments, the polynomial series is truncated at apoint where the resulting output is visually lossless based on a humanvisual system model.

In embodiments further described below, the polynomial series is atruncated Chebyshev series. In these embodiments, the use of Chebyshevpolynomials may be desirable because i) they minimize Runge's phenomenonwhen approximating in an interval, which is important since in practicemost displays are located near the minimum end of the examined dynamicrange hulls; ii) they can be quickly computed numerically; and iii) theerror of the approximated function as compared to the original may beeasily estimated from the coefficients, thereby providing a stoppingpoint. However, other suitable polynomial series known in the art may beused.

Following approximation of the CDR functions using a finite set ofcoefficients with a polynomial basis, at operation 232 the polynomialcoefficients are represented in an image format, which allowsapplication of a video codec to the data. For example, in one embodimentthe polynomial coefficients may be reorganized into monochrome videosequences. In embodiments, the representation of the polynomialcoefficients in an image format may depend on the video codec (e.g,MPEG-4, H.264, etc.) that is subsequently used to encode the data.

Thereafter, at operation 233 the coefficient images (i.e., image dataformatted polynomial coefficients) may be encoded using a correspondingvideo codec such as MPEG-4 or H.264, thus providing additionalcompression and improving data bitrates. Additionally, at operation 233minimum dynamic range graded content 202 and maximum dynamic rangegraded content 203 may be compressed and encoded using video codecsknown in the art for compressing LDR and HDR content (e.g., MPEGformat). In embodiments, content 202 and 203 may be jointly anddependently encoded by making use of inter-redundancies between the twosignals. For example, in particular embodiments the two contents may beencoded as base and enhancement layers using scalable video coding (SVC)methods known in the art. Alternatively, in other embodiments minimumdynamic range graded content 202 and maximum dynamic range gradedcontent 203 may be encoded separately (e.g., using H.264).

In embodiments, a mathematical implementation of method 230 using theaforementioned lumipaths g^(p,f) may proceed as follows. The humanvisual system may be modeled using a threshold-versus-intensity (tvi)function that computes an approximate threshold luminance, given a levelof luminance adaptation L_(a). The tvi function may be computed byfinding the peak contrast sensitivity at each luminance level as shownby Equation (6):

$\begin{matrix}{{{tvi}\left( L_{a}^{p} \right)} = \frac{L_{a}^{p}}{\max_{x}\left( {{CSF}\left( {x,L_{a}^{p}} \right)} \right)}} & (6)\end{matrix}$Where CSF is the contrast sensitivity function, and L_(a) ^(p) is theadaptation luminance for a pixel p. In this implementation it is assumedthat the human eye can adapt perfectly to a single pixel p.

Given a lumipath g^(p,f), it may be approximated at a given pixel in aperceptually lossless way by a truncated Chebyshev series

^(p,f) if ∥g^(p,f)−

^(p,f)∥_(∞)<tvi(L_(a) ^(p)) is satisfied, i.e. the deviation is smallerthan the threshold computed by the model of the human visual system. Thetruncated Chebyshev series may be represented by Equation (7):

$\begin{matrix}{{{\overset{\_}{g}}^{p,f}(x)} = {\sum\limits_{k = 0}^{N_{p,f}}{c_{k}^{p,f}{\psi_{k}(x)}}}} & (7)\end{matrix}$Where ψ_(k)(x) is the k-th Chebyshev polynomial, c_(k) ^(p,f) is thecorresponding Chebyshev coefficient at pixel p of frame f, and N_(p,f)is the smallest degreed required to obtain an error ∥g^(p,f)−

^(p,f)∥_(∞) which is smaller than tvi(L_(a) ^(p)). This defines aperceptually lossless approximation of g^(p,f) which is determined byN_(p,f)+1 coefficients c₀ ^(p), . . . , c_(N) _(p,f) ^(p,f).

For computing the Chebyshev series, the domain and range of alllumipaths is scaled such that they all lie in the Chebyshev domaing_(p,f):[−1,1]→[−1,1]. Because each basis polynomial ψ_(k)(x) has adomain

:=[−1, 1] and its range ψ_(k)(

) is also a subset of [−1,1], the total ∥g^(p)−

_(p)∥_(∞) error of the approximation is bounded by the sum of theabsolute values of the infinite remaining coefficients of the series. Inembodiments, a stopping criterion for the coefficients may be given bythe sum of the absolute value of a small number of elements. Forexample, the series may be truncated when the absolute sum of the nextthree elements is below an allowed error threshold. An example of anapproximation of a function by Chebyshev polynomials of different ordersis illustrated by FIG. 10. The absolute value of the error between theoriginal function and the reconstructed representation is shown in thebottom scale of FIG. 10.

Following determination of the Chebyshev coefficients (c₀ ^(p), . . . ,c_(N) _(p,f) ^(p,f)) for an approximated but visually losslessrepresentation of lumipath

^(p,f), the coefficients may be quantized and reorganized intomonochrome video sequences. The maximum degree of N:=max_(p,f)N_(p,f)and set c_(k) ^(p,f):=0 for k>N_(p,f) may be computed, which leads to arepresentation

^(p,f)(x)=Σ_(k=0) ^(N)c_(k) ^(p,f)ψ_(k)(x) of the function described inEquation (7), but with a fixed parameter N. Each lumipath

^(p,f) is now specified by an N-tuple of Equation (8):c ^(p,f):=(c ₁ ^(p,f) , . . . ,c _(N) ^(p,f))  (8)To obtain an image-like representation, the tuples c^(p,f) of all pixelsof a frame are represented by coefficient matrices C_(k) ^(f)∈

^(h×w) for 1<k<N which by construction have the same pixel resolutionh×w as I_(α) ^(f) and I_(β) ^(f). All entries of all matrices C_(k) ^(f)may then be uniformly quantized to a particular bit depth to obtain Nmatrices C _(k) ^(f). In embodiments, the bit depth may be selecteddepending on the maximum bit depth for images which are supported by thevideo codec used for compression. For example, in this exampleimplementation the entries of all matrices may be quantized to 8-bitintegers because it corresponds to the maximum bit depth for imageswhich are supported for compression by the main profile of H.264.

FIG. 11 illustrates the first eight coefficient images C _(k) ¹ of aframe of a video sequence. As illustrated, most of the information isconcentrated within the first few coefficients. The energy and variancein the coefficient images drops rapidly with increasing coefficientindex. Moreover, coefficients may have uniform values over large imageregions. Accordingly, the information content of coefficient images andvideos may generally be relatively limited in practice as compared tothe images and videos themselves, making them very compressible.

Thereafter a compressed representation of the lumipaths may be obtainedby storing: 1) an integer value representing the degree N, 2) twofloating point-values representing the minimum and maximum value usedfor bit depth (e.g., 8-bit) quantization, and 3) an encodedrepresentation of the image sequences C _(k) ¹, . . . , C _(k) ^(F) fork=1, . . . , N which is obtained by encoding the coefficient imagesusing the video codec (e.g., H.264).

Following video compression and encoding of the CDR video at operation230, the output content includes encoded minimum dynamic range gradedcontent 207, encoded maximum dynamic range graded content 208, andencoded CDR video metadata 209. Referring back to FIG. 3, this contentmay subsequently be distributed at operation 240. In embodiments, theCDR video content may be distributed as an over-the-air broadcasttelevision signal, a satellite television network signal, or a cabletelevision network signal. Alternatively, the CDR video content may betransmitted by a content server over a computer network. In yet furtherembodiments, the CDR video content may be distributed using physicalmedia such as a solid state drive, magnetic tape, cartridge, a Blu-raydisc, etc.

FIG. 12 illustrates a receiver side method 700 for decoding anddisplaying received CDR video content. As illustrated, the received CDRcontent may include the video encoded minimum dynamic range gradedcontent 207, maximum dynamic range graded content 208, and CDR videometadata 209. Although illustrated as having separate reference numbersin example FIG. 12, it should be noted that the received maximum dynamicrange graded content and minimum dynamic range graded may arrive jointlyencoded (e.g., as a base layer and enhancement layer based on SVCtechniques).

At operation 702, the received content is decoded using a suitable videocompression codec (e.g., H.264, MPEG-4, etc.). For example, inparticular embodiments where the minimum graded content and maximumgraded content were jointly encoded as base and enhancement layers usinga SVC codec, the content may be decoded using the SVC codec.Alternatively, in other embodiments minimum dynamic range graded content202 and maximum dynamic range graded content 203 may be decodedseparately (e.g., using H.264). In one embodiment, CDR video metadata209 may be decoded using the same codec used to decode content 207 and208.

Subsequently, at operation 704 the receiver creates a suitable dynamicrange representation of the video 707 based on the decoded content and aknown dynamic range 705 of the display that will display the content. Aswould be appreciated by one having skill in the art, the receiver mayreconstruct a lumipath for each pixel based on the polynomial vector ofcoefficients and knowledge of the algorithms used to create the CDRmetadata, such as the polynomial series used to represent the lumipathfunctions for each pixel of each frame. Thereafter, given the decodedmaximum and minimum graded images, the decoded and reconstructedlumipaths for each pixel of the image, and the display dynamic range705, a corresponding lumipath may be evaluated for each pixel of eachimage to define the luminance of that pixel for the display, thereby aobtaining a suitable dynamic range representation of the video 707.

Although methods described herein have been primarily described withreference to the creation, compression, distribution, and receipt of CDRvideo, one having skill in the art would appreciate that they mayequally apply to the creation of CDR images such as CDR photographs orcomputer-generated graphics. For example, in various embodiments a CDRimage may be created by grading a source image for maximum and minimumdynamic ranges, and defining a luminance of each pixel of the image as acontinuous function based on the minimum and maximum dynamic rangegradings. As another example, the graphical user interface of FIGS.7A-7B could be adapted for the creation of CDR images by allowing anartist to simultaneously display and modify a plurality of dynamic rangegraded versions of a particular image (e.g., by adding a new mode 609for images or using the existing frame mode).

Similarly, in various embodiments the CDR image may be compressed byapproximating the CDR functions corresponding to each pixel of the imageusing a polynomial series that is truncated after a certain number ofcoefficients. Additionally, the compressed CDR image may be encodedusing a suitable codec. Furthermore, the encoded CDR image may bedistributed to a receiver that decodes and displays the image using asuitable codec.

FIG. 13 illustrates an example computing module that may be used toimplement various features of the systems and methods disclosed herein.As used herein, the term module might describe a given unit offunctionality that can be performed in accordance with one or moreembodiments of the present application. As used herein, a module mightbe implemented utilizing any form of hardware, software, or acombination thereof. For example, one or more processors, controllers,ASICs, PLAs, PALs, CPLDs, FPGAs, logical components, software routinesor other mechanisms might be implemented to make up a module. Inimplementation, the various modules described herein might beimplemented as discrete modules or the functions and features describedcan be shared in part or in total among one or more modules. In otherwords, as would be apparent to one of ordinary skill in the art afterreading this description, the various features and functionalitydescribed herein may be implemented in any given application and can beimplemented in one or more separate or shared modules in variouscombinations and permutations. Even though various features or elementsof functionality may be individually described or claimed as separatemodules, one of ordinary skill in the art will understand that thesefeatures and functionality can be shared among one or more commonsoftware and hardware elements, and such description shall not requireor imply that separate hardware or software components are used toimplement such features or functionality.

Where components or modules of the application are implemented in wholeor in part using software, in one embodiment, these software elementscan be implemented to operate with a computing or processing modulecapable of carrying out the functionality described with respectthereto. One such example computing module is shown in FIG. 13. Variousembodiments are described in terms of this example-computing module1000. After reading this description, it will become apparent to aperson skilled in the relevant art how to implement the applicationusing other computing modules or architectures.

Referring now to FIG. 13, computing module 1000 may represent, forexample, computing or processing capabilities found within desktop,laptop, notebook, and tablet computers; hand-held computing devices(tablets, PDA's, smart phones, cell phones, palmtops, etc.); mainframes,supercomputers, workstations or servers; or any other type ofspecial-purpose or general-purpose computing devices as may be desirableor appropriate for a given application or environment. Computing module1000 might also represent computing capabilities embedded within orotherwise available to a given device. For example, a computing modulemight be found in other electronic devices such as, for example, digitalcameras, navigation systems, cellular telephones, portable computingdevices, modems, routers, WAPs, terminals and other electronic devicesthat might include some form of processing capability.

Computing module 1000 might include, for example, one or moreprocessors, controllers, control modules, or other processing devices,such as a processor 1004. Processor 1004 might be implemented using ageneral-purpose or special-purpose processing engine such as, forexample, a microprocessor, controller, or other control logic. In theillustrated example, processor 1004 is connected to a bus 1002, althoughany communication medium can be used to facilitate interaction withother components of computing module 1000 or to communicate externally.

Computing module 1000 might also include one or more memory modules,simply referred to herein as main memory 1008. For example, preferablyrandom access memory (RAM) or other dynamic memory, might be used forstoring information and instructions to be executed by processor 1004.Main memory 1008 might also be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 1004. Computing module 1000 might likewise includea read only memory (“ROM”) or other static storage device coupled to bus1002 for storing static information and instructions for processor 1004.

The computing module 1000 might also include one or more various formsof information storage mechanism 1010, which might include, for example,a media drive 1012 and a storage unit interface 1020. The media drive1012 might include a drive or other mechanism to support fixed orremovable storage media 1014. For example, a hard disk drive, a solidstate drive, a magnetic tape drive, an optical disk drive, a CD, DVD, orBlu-ray drive (R or RW), or other removable or fixed media drive mightbe provided. Accordingly, storage media 1014 might include, for example,a hard disk, a solid state drive, magnetic tape, cartridge, opticaldisk, a CD, DVD, Blu-ray or other fixed or removable medium that is readby, written to or accessed by media drive 1012. As these examplesillustrate, the storage media 1014 can include a computer usable storagemedium having stored therein computer software or data.

In alternative embodiments, information storage mechanism 1010 mightinclude other similar instrumentalities for allowing computer programsor other instructions or data to be loaded into computing module 1000.Such instrumentalities might include, for example, a fixed or removablestorage unit 1022 and an interface 1020. Examples of such storage units1022 and interfaces 1020 can include a program cartridge and cartridgeinterface, a removable memory (for example, a flash memory or otherremovable memory module) and memory slot, a PCMCIA slot and card, andother fixed or removable storage units 1022 and interfaces 1020 thatallow software and data to be transferred from the storage unit 1022 tocomputing module 1000.

Computing module 1000 might also include a communications interface1024. Communications interface 1024 might be used to allow software anddata to be transferred between computing module 1000 and externaldevices. Examples of communications interface 1024 might include a modemor softmodem, a network interface (such as an Ethernet, networkinterface card, WiMedia, IEEE 802.XX or other interface), acommunications port (such as for example, a USB port, IR port, RS232port Bluetooth® interface, or other port), or other communicationsinterface. Software and data transferred via communications interface1024 might typically be carried on signals, which can be electronic,electromagnetic (which includes optical) or other signals capable ofbeing exchanged by a given communications interface 1024. These signalsmight be provided to communications interface 1024 via a channel 1028.This channel 1028 might carry signals and might be implemented using awired or wireless communication medium. Some examples of a channel mightinclude a phone line, a cellular link, an RF link, an optical link, anetwork interface, a local or wide area network, and other wired orwireless communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to transitory ornon-transitory media such as, for example, memory 1008, storage unit1020, media 1014, and channel 1028. These and other various forms ofcomputer program media or computer usable media may be involved incarrying one or more sequences of one or more instructions to aprocessing device for execution. Such instructions embodied on themedium, are generally referred to as “computer program code” or a“computer program product” (which may be grouped in the form of computerprograms or other groupings). When executed, such instructions mightenable the computing module 1000 to perform features or functions of thepresent application as discussed herein.

Although described above in terms of various exemplary embodiments andimplementations, it should be understood that the various features,aspects and functionality described in one or more of the individualembodiments are not limited in their applicability to the particularembodiment with which they are described, but instead can be applied,alone or in various combinations, to one or more of the otherembodiments of the application, whether or not such embodiments aredescribed and whether or not such features are presented as being a partof a described embodiment. Thus, the breadth and scope of the presentapplication should not be limited by any of the above-describedexemplary embodiments.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read as meaning “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; the terms “a” or“an” should be read as meaning “at least one,” “one or more” or thelike; and adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known” and terms of similar meaning should not be construedas limiting the item described to a given time period or to an itemavailable as of a given time, but instead should be read to encompassconventional, traditional, normal, or standard technologies that may beavailable or known now or at any time in the future. Likewise, wherethis document refers to technologies that would be apparent or known toone of ordinary skill in the art, such technologies encompass thoseapparent or known to the skilled artisan now or at any time in thefuture.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not of limitation. Likewise, the various diagrams maydepict an example architectural or other configuration for thedisclosure, which is done to aid in understanding the features andfunctionality that can be included in the disclosure. The disclosure isnot restricted to the illustrated example architectures orconfigurations, but the desired features can be implemented using avariety of alternative architectures and configurations. Indeed, it willbe apparent to one of skill in the art how alternative functional,logical or physical partitioning and configurations can be implementedto implement the desired features of the present disclosure. Also, amultitude of different constituent module names other than thosedepicted herein can be applied to the various partitions. Additionally,with regard to flow diagrams, operational descriptions and methodclaims, the order in which the steps are presented herein shall notmandate that various embodiments be implemented to perform the recitedfunctionality in the same order unless the context dictates otherwise.

Although the disclosure is described above in terms of various exemplaryembodiments and implementations, it should be understood that thevarious features, aspects and functionality described in one or more ofthe individual embodiments are not limited in their applicability to theparticular embodiment with which they are described, but instead can beapplied, alone or in various combinations, to one or more of the otherembodiments of the disclosure, whether or not such embodiments aredescribed and whether or not such features are presented as being a partof a described embodiment. Thus, the breadth and scope of the presentdisclosure should not be limited by any of the above-described exemplaryembodiments.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read as meaning “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; the terms “a” or“an” should be read as meaning “at least one,” “one or more” or thelike; and adjectives such as “conventional,” “traditional,” “normal,”“standard,” “known” and terms of similar meaning should not be construedas limiting the item described to a given time period or to an itemavailable as of a given time, but instead should be read to encompassconventional, traditional, normal, or standard technologies that may beavailable or known now or at any time in the future. Likewise, wherethis document refers to technologies that would be apparent or known toone of ordinary skill in the art, such technologies encompass thoseapparent or known to the skilled artisan now or at any time in thefuture.

The presence of broadening words and phrases such as “one or more,” “atleast,” “but not limited to” or other like phrases in some instancesshall not be read to mean that the narrower case is intended or requiredin instances where such broadening phrases may be absent. The use of theterm “module” does not imply that the components or functionalitydescribed or claimed as part of the module are all configured in acommon package. Indeed, any or all of the various components of amodule, whether control logic or other components, can be combined in asingle package or separately maintained and can further be distributedin multiple groupings or packages or across multiple locations.

Additionally, the various embodiments set forth herein are described interms of exemplary block diagrams, flow charts and other illustrations.As will become apparent to one of ordinary skill in the art afterreading this document, the illustrated embodiments and their variousalternatives can be implemented without confinement to the illustratedexamples. For example, block diagrams and their accompanying descriptionshould not be construed as mandating a particular architecture orconfiguration.

What is claimed is:
 1. A method of compressing and encoding a continuousdynamic range video, the method comprising: receiving: a minimum dynamicrange graded image corresponding to a video frame, a maximum dynamicrange graded image corresponding to the video frame, and metadataincluding a separate continuous dynamic range function for each pixel ofthe video frame; representing each of the continuous dynamic rangefunctions using a corresponding vector of coefficients of a truncatedpolynomial series approximating the continuous dynamic range function;and representing the vectors of coefficients in an image format.
 2. Themethod of claim 1, wherein the polynomial series is a Chebyshevpolynomial series.
 3. The method of claim 1, further comprising encodingan image-format representation of the vectors of coefficients with avideo codec.
 4. The method of claim 1, further comprising jointly anddependently encoding the minimum dynamic range graded image and themaximum dynamic range graded image based on redundancies between theminimum dynamic range graded image and the maximum dynamic range gradedimage.
 5. A method of decoding a continuous dynamic range image fordisplay on a display having an associated dynamic range, the methodcomprising: receiving an encoded continuous dynamic range imagecomprising: a minimum dynamic range graded version of an image, amaximum dynamic range graded version of the image, and continuousdynamic range metadata corresponding to the image; decoding thecontinuous dynamic image using a codec to generate a decoded continuousdynamic range image; and creating a particular dynamic rangerepresentation of the image based on the decoded continuous dynamicrange image and the dynamic range associated with the display.
 6. Themethod of claim 5, wherein the image corresponds to a video frame, andwherein the encoded continuous dynamic range image is a video encodedcontinuous dynamic range video frame.
 7. The method of claim 6, whereinthe minimum dynamic range graded version of the image and the maximumdynamic range graded version of the image are jointly encoded based on ascalable video coding codec, and wherein decoding the continuous dynamicrange image comprises decoding the jointly encoded minimum dynamic rangegraded version of the image and maximum dynamic range graded version ofthe image using the scalable video coding codec.
 8. The method of claim5, wherein the continuous dynamic range metadata comprises metadatadefining a luminance of a pixel of the image as coefficients of atruncated polynomial series approximating a continuous function definingthe luminance of the pixel between a minimum dynamic range and a maximumdynamic range.
 9. The method of claim 8, wherein the polynomial seriesis a Chebyshev series.
 10. The method of claim 8, wherein the continuousfunction is a lumipath.
 11. A system, comprising: a display having anassociated dynamic range; one or more processors; and one or morenon-transitory computer-readable media operatively coupled to at leastone of the one or more processors and having instructions stored thereonthat, when executed by the at least one of the one or more processors,cause the at least one of the one or more processors to: receive anencoded continuous dynamic range image comprising: a minimum dynamicrange graded version of an image; a maximum dynamic range graded versionof the image; and continuous dynamic range metadata corresponding to theimage, decode the encoded continuous dynamic range image, create aparticular dynamic range representation of the image based on thedynamic range associated with the display, and display the particulardynamic range representation of the image on the display.
 12. The systemof claim 11, wherein the image corresponds to a video frame, and whereinthe encoded continuous dynamic range image is a video encoded continuousdynamic range video frame.
 13. The system of claim 12, wherein theminimum dynamic range graded version of the image and the maximumdynamic range graded version of the image are jointly encoded based on ascalable video coding codec, and wherein decoding the continuous dynamicrange image comprises decoding the jointly encoded minimum dynamic rangegraded version of the image and maximum dynamic range graded version ofthe image using the scalable video coding codec.
 14. The system of claim11, wherein the continuous dynamic range metadata comprises metadatadefining a luminance of a pixel of the image as coefficients of atruncated polynomial series approximating a continuous function definingthe luminance of the pixel between a minimum dynamic range and a maximumdynamic range.
 15. The system of claim 14, wherein the polynomial seriesis a Chebyshev series.
 16. The system of claim 14, wherein thecontinuous function is a lumipath.
 17. The method of claim 1, furthercomprising separately encoding the minimum dynamic range graded imageand the maximum dynamic range graded image.
 18. The method of claim 3,wherein: in the image-format representation, the vectors of coefficientsare represented by coefficient matrices; and encoding the image-formatrepresentation of the vector of coefficients with the video codeccomprises quantizing the coefficient matrices to a given bit depth. 19.The method of claim 18, wherein the given bit depth is a maximum bitdepth supported by the video codec.
 20. The method of claim 3, whereinthe video codec is one of a MPEG-4 codec or a H.264 codec.